Science and Research Content

Takeaways from the Text Analytics Forum -


The biggest takeaway from the Text Analytics Forum was the lack of a best answer to using text analytics in enterprises, notes Ahren Lehnert, Senior Manager, Text Analytics Solutions at Synaptica. In addition, proposals on how to get text analytics adopted in an enterprise, where this function should live, and who should perform it, were missing in the presentations.

Text analytics has been around for decades. However, with the rise of big data, server capacity, and machine learning, we are seeing more interest and adoption within an enterprise. Tools and techniques can be selected and developed based on the organization and for each use case. Additionally, it may be possible to template some of the tools and processes. However, the unique demands of unstructured or semi-structured text will necessitate clever approaches to each situation. This may probably be the reason that there is no single answer or the best answer to using text analytics in enterprises.

Though text analytics is an established discipline, formal propositions for widespread use in enterprises are rare. The lack of guidance may simply be a matter of maturity. This contrast is noticeable in forums and conferences. There will be multiple presentations on gaining traction for knowledge management and taxonomy projects and adoption from end users. However, presentations on text analytics in enterprises will be mostly presented by a variety of consultants, IT or business users who learned text analytics as part of their role. In all probability, this will change in the near future. We might witness more presentations on getting text analytics projects started and adopted. There could also be an increase in the number of enterprises hiring text analysts for specific roles in the future.

Click here to read more about the new techniques and the applications text analytics processes may power, the relevance of manual tagging in the age of automation, and the hybrid approaches to machine learning.

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